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Investigation of the stochastic subspace identification method for on-line wind turbine tower monitoring
Author(s): Kaoshan Dai; Ying Wang; Wensheng Lu; Jianze Wang; Xiaosong Ren; Zhenhua Huang
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Paper Abstract

Structural health monitoring (SHM) of wind turbines has been applied in the wind energy industry to obtain their real-time vibration parameters and to ensure their optimum performance. For SHM, the accuracy of its results and the efficiency of its measurement methodology and data processing algorithm are the two major concerns. Selection of proper measurement parameters could improve such accuracy and efficiency. The Stochastic Subspace Identification (SSI) is a widely used data processing algorithm for SHM. This research discussed the accuracy and efficiency of SHM using SSI method to identify vibration parameters of on-line wind turbine towers. Proper measurement parameters, such as optimum measurement duration, are recommended.

Paper Details

Date Published: 19 April 2017
PDF: 7 pages
Proc. SPIE 10169, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017, 101692F (19 April 2017); doi: 10.1117/12.2259759
Show Author Affiliations
Kaoshan Dai, Tongji Univ. (China)
Key Lab. of Energy Engineering Safety and Disaster Mechanics (China)
Ying Wang, Tongji Univ. (China)
Wensheng Lu, Tongji Univ. (China)
Jianze Wang, Tongji Univ. (China)
Xiaosong Ren, Tongji Univ. (China)
Zhenhua Huang, Univ. of North Texas (United States)


Published in SPIE Proceedings Vol. 10169:
Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017
H. Felix Wu; Andrew L. Gyekenyesi; Peter J. Shull; Tzu-Yang Yu, Editor(s)

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